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arXiv · 2309.10299

Using fine-tuning and min lookahead beam search to improve Whisper

Andrea Do·Oscar Brown·Zhengjie Wang·Nikhil Mathew·Zixin Liu·Jawwad Ahmed·Cheng Yu
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Abstract

The performance of Whisper in low-resource languages is still far from perfect. In addition to a lack of training data on low-resource languages, we identify some limitations in the beam search algorithm used in Whisper. To address these issues, we fine-tune Whisper on additional data and propose an improved decoding algorithm. On the Vietnamese language, fine-tuning Whisper-Tiny with LoRA leads to an improvement of 38.49 in WER over the zero-shot Whisper-Tiny setting which is a further reduction of 1.45 compared to full-parameter fine-tuning. Additionally, by using Filter-Ends and Min Lookahead decoding algorithms, the WER reduces by 2.26 on average over a range of languages compared to standard beam search. These results generalise to larger Whisper model sizes. We also prove a theorem that Min Lookahead outperforms the standard beam search algorithm used in Whisper.

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Andrea Do, Oscar Brown, Zhengjie Wang, Nikhil Mathew, Zixin Liu, Jawwad Ahmed, Cheng Yu. 2023-09-19. Using fine-tuning and min lookahead beam search to improve Whisper. https://arxiv.org/abs/2309.10299

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